Image Segmentation Through Contextual Clustering

. Several interesting strategies are adopted by the well-known Pappas clustering algorithm to segment smooth images. These include exploitation of contextual information to model both class conditional densities and a priori knowledge in a Bayesian framework. Deficiencies of this algorithm are that: i) it removes from the scene any genuine but small region; and ii) its feature-preserving capability largely depends on a user-defined smoothing parameter. This parameter is equivalent to a clique potential of a Markov Random Field model employed to capture known stochastic components of the labeled scene. In this paper a modified version of the Pappas segmentation algorithm is proposed to process smooth and noiseless images requiring enhanced pattern-preserving capability. In the proposed algorithm: iii) no spatial continuity in pixel labeling is enforced to capture known stochastic components of the labeled scene; iv) local intensity parameters, pixel labels, and global intensity paramete..